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Knowledge bases (KBs) are paramount in NLP. We employ multiview learning forincreasing accuracy and coverage of entity type information in KBs. We rely ontwo metaviews: language and representation. For language, we considerhigh-resource and low-resource languages from Wikipedia. For representation, weconsider representations based on the context distribution of the entity (i.e.,on its embedding), on the entity's name (i.e., on its surface form) and on itsdescription in Wikipedia. The two metaviews language and representation can befreely combined: each pair of language and representation (e.g., Germanembedding, English description, Spanish name) is a distinct view. Ourexperiments on entity typing with fine-grained classes demonstrate theeffectiveness of multiview learning. We release MVET, a large multiview - and,in particular, multilingual - entity typing dataset we created. Mono- andmultilingual fine-grained entity typing systems can be evaluated on thisdataset.

Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing